Intelligent Detection of Suspicious Human Activities through CNN Integration

Intelligent Detection of Suspicious Human Activities through CNN Integration

*Note: Sub-titles are not captured in Xplore and should not be used

1st Harshit Nautiyal
Department of Computing Technologies

SRM Institute of Science and Technology Chennai, India

hr2067@srmist.edu.in

2nd Sai Teja
Department of Computing Technologies

 SRM Institute of Science and Technology Chennai, India

ck3390@srmist.edu.in

3rd Dr. Thilagavathy R
Department of Computing Technologies

SRM Institute of Science and Technology Chennai, India

thilagar2@srmist.edu.in

Abstract—With the unprecedented development of technolo- gies, there has been the trend of rapid deployment of surveillance systems in multiple domains, such as security and healthcare, and public safety. In this paper, a new method of suspicious human activity detection based on Convolutional Neural Net- works (CNNs) combined with Long Short-Term Memory (LSTM) networks is proposed. The presented system seeks to improve the robustness and speed of anomaly detection across real-time video streams. Leveraging CNNs for spatial feature extraction and LSTMs for temporal sequence modeling, the proposed system efficiently detects suspicious activities in real time.

Index Terms—Suspicious Activity Detection, Human Activity Recognition, Convolutional Neural Networks, Deep Learning, Video Surveillance